Spherical Harmonics For Shape-Constrained 3d Cell Segmentation | IEEE Conference Publication | IEEE Xplore

Spherical Harmonics For Shape-Constrained 3d Cell Segmentation


Abstract:

Recent microscopy imaging techniques allow to precisely analyze cell morphology in 3D image data. To process the vast amount of image data generated by current digitized ...Show More

Abstract:

Recent microscopy imaging techniques allow to precisely analyze cell morphology in 3D image data. To process the vast amount of image data generated by current digitized imaging techniques, automated approaches are demanded more than ever. Segmentation approaches used for morphological analyses, however, are often prone to produce unnaturally shaped predictions, which in conclusion could lead to inaccurate experimental outcomes. In order to minimize further manual interaction, shape priors help to constrain the predictions to the set of natural variations. In this paper, we show how spherical harmonics can be used as an alternative way to inherently constrain the predictions of neural networks for the segmentation of cells in 3D microscopy image data. Benefits and limitations of the spherical harmonic representation are analyzed and final results are compared to other state-of-the-art approaches on two different data sets.
Date of Conference: 13-16 April 2021
Date Added to IEEE Xplore: 25 May 2021
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Conference Location: Nice, France

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